In this paper, we present a novel no-reference (NR) metric to assess the quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering the key human visual sensitivity factors such as, edge amplitude, edge length, background activity and background luminance. The extracted features with the subjective test results are used to train a multi-layer perceptron (MLP) neural network. Experimental results show that the prediction of the trained neural network is very close to the mean opinion score (MOS). The subjective test results of the proposed metric are compared with the Wang-Bovik's NR blockiness metric. Further, this metric can be extended to assess the quality of the MPLG/H.26x compressed videos.
An HVS-based no-reference perceptual quality assessment of JPEG coded images using neural networks
01.01.2005
217502 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
An HVS-Based No-Reference Perceptual Quality Assessment of JPEG Coded Images using Neural Networks
British Library Conference Proceedings | 2005
|Blockiness in JPEG-coded images
British Library Conference Proceedings | 1999
|Local region-based image quality assessment independent of JPEG and JPEG2000 coded color images
British Library Online Contents | 2008
|British Library Online Contents | 2011
|Enlargement method for JPEG-coded images with the prediction of high-frequency components [5014-05]
British Library Conference Proceedings | 2003
|